


import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class        lowerCAmelCase__ (					lowercase__  ):






						"""simple docstring"""
						__UpperCAmelCase       :						Union[str, Any]    	=			['''image_processor''', '''tokenizer''']
						__UpperCAmelCase       :						Any    	=			'''FlavaImageProcessor'''
						__UpperCAmelCase       :						str    	=			('''BertTokenizer''', '''BertTokenizerFast''')




						def __init__(       self ,				a_=None ,				a_=None ,				**a_      ):
							lowerCamelCase_					:				List[str]              =  None
							if "feature_extractor" in kwargs:
								warnings.warn(
								    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
								    " instead." ,				SCREAMING_SNAKE_CASE_ ,				)
								lowerCamelCase_					:				Optional[int]              =  kwargs.pop("feature_extractor"      )

							lowerCamelCase_					:				Tuple              =  image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
								raise ValueError("You need to specify an `image_processor`."      )
							if tokenizer is None:
								raise ValueError("You need to specify a `tokenizer`."      )

							super().__init__(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )
							lowerCamelCase_					:				Optional[int]              =  self.image_processor




						def __call__(       self ,				a_ = None ,				a_ = None ,				a_ = True ,				a_ = False ,				a_ = False ,				a_ = None ,				a_ = 0 ,				a_ = None ,				a_ = None ,				a_ = None ,				a_ = None ,				a_ = None ,				a_ = False ,				a_ = False ,				a_ = False ,				a_ = False ,				a_ = True ,				a_ = None ,				**a_ ,				):


							if text is None and images is None:
								raise ValueError("You have to specify either text or images. Both cannot be none."      )

							if text is not None:
								lowerCamelCase_					:				Union[str, Any]              =  self.tokenizer(
								    text=SCREAMING_SNAKE_CASE_ ,				add_special_tokens=SCREAMING_SNAKE_CASE_ ,				padding=SCREAMING_SNAKE_CASE_ ,				truncation=SCREAMING_SNAKE_CASE_ ,				max_length=SCREAMING_SNAKE_CASE_ ,				stride=SCREAMING_SNAKE_CASE_ ,				pad_to_multiple_of=SCREAMING_SNAKE_CASE_ ,				return_token_type_ids=SCREAMING_SNAKE_CASE_ ,				return_attention_mask=SCREAMING_SNAKE_CASE_ ,				return_overflowing_tokens=SCREAMING_SNAKE_CASE_ ,				return_special_tokens_mask=SCREAMING_SNAKE_CASE_ ,				return_offsets_mapping=SCREAMING_SNAKE_CASE_ ,				return_length=SCREAMING_SNAKE_CASE_ ,				verbose=SCREAMING_SNAKE_CASE_ ,				return_tensors=SCREAMING_SNAKE_CASE_ ,				**SCREAMING_SNAKE_CASE_ ,				)
							if images is not None:
								lowerCamelCase_					:				List[Any]              =  self.image_processor(
								    SCREAMING_SNAKE_CASE_ ,				return_image_mask=SCREAMING_SNAKE_CASE_ ,				return_codebook_pixels=SCREAMING_SNAKE_CASE_ ,				return_tensors=SCREAMING_SNAKE_CASE_ ,				**SCREAMING_SNAKE_CASE_ ,				)

							if text is not None and images is not None:
								encoding.update(SCREAMING_SNAKE_CASE_      )
								return encoding
							elif text is not None:
								return encoding
							else:
								return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_      ) ,				tensor_type=SCREAMING_SNAKE_CASE_      )




						def 	_UpperCamelCase					(       self ,				*a_ ,				**a_      ):
							return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,				**SCREAMING_SNAKE_CASE_      )




						def 	_UpperCamelCase					(       self ,				*a_ ,				**a_      ):
							return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,				**SCREAMING_SNAKE_CASE_      )




						@property
						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				Dict              =  self.tokenizer.model_input_names
							lowerCamelCase_					:				Tuple              =  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




						@property
						def 	_UpperCamelCase					(       self      ):
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				SCREAMING_SNAKE_CASE_ ,				)
							return self.image_processor_class




						@property
						def 	_UpperCamelCase					(       self      ):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				SCREAMING_SNAKE_CASE_ ,				)
							return self.image_processor



"""simple docstring"""






import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar



class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  logging.get_logger()

				# the current default level is logging.WARNING
				lowerCAmelCase												=  logging.get_verbosity()

				logging.set_verbosity_error()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_warning()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_info()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_debug()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  logging.get_verbosity()

				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				lowerCAmelCase												=  '''Testing 1, 2, 3'''

				# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
				if level_origin <= logging.WARNING:
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,msg + '''\n'''			)

				# this is setting the level for all of `transformers.*` loggers
				logging.set_verbosity_error()

				# should not be able to log warnings
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_			)
				self.assertEqual(cl.out      ,''''''			)

				# should be able to log warnings again
				logging.set_verbosity_warning()
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_			)
				self.assertEqual(cl.out      ,msg + '''\n'''			)

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_			)






			@mockenv(TRANSFORMERS_VERBOSITY='''error'''			)
			def 					UpperCAmelCase							(self  :	Any			)       ->	str:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()
				# this action activates the env var
				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)

				lowerCAmelCase												=  os.getenv('''TRANSFORMERS_VERBOSITY'''      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  logging.log_levels[env_level_str]

				lowerCAmelCase												=  logging.get_verbosity()
				self.assertEqual(
				    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,F"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""      ,)

				# restore to the original level
				lowerCAmelCase												=  ''''''
				transformers.utils.logging._reset_library_root_logger()






			@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''			)
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Tuple:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()
				lowerCAmelCase												=  logging.logging.getLogger()
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					# this action activates the env var
					logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''      ,cl.out			)

				# no need to restore as nothing was changed






			def 					UpperCAmelCase							(self  :	Dict			)       ->	Any:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()

				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				lowerCAmelCase												=  '''Testing 1, 2, 3'''

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''			):
					# nothing should be logged as env var disables this method
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,''''''			)

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''			):
					# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,msg + '''\n'''			)





def __magic_name__	(				)					->						List[Any]:

	'''simple docstring'''




	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()


